Trajectory Control Research Articles

A sliding mode based foot-end trajectory consensus control method with variable topology for legged motion of heavy-duty robot

Rational foot-end trajectory planning and control are of great significance for stable-legged walking of heavy-duty multi-legged robots. To achieve a fast, active, and compliant response of the leg actuator to disturbances for improvement of the stability and flexibility of the heavy-duty legged robot system during continuous walking on rough roads, a legged consensus control method (LCC) is proposed. Firstly, the LCC includes a foot-end trajectory planner model for designing the trajectory during the swing phase to ensure that the robot’s feet are always in a safe workspace during legged motion with continuously variable direction. Secondly, LCC constructs a consensus control method for encoding foot-end position and velocity consensus error based on variable topology networks. Six legs are treated as six intelligent agents and divided into two fully connected networks: the swing phase and stance phase, to achieve smooth and consistent motion that satisfies the geometric constraints of the robot. The foot-end agent can switch between swing and stance groups according to the state of the contact with the environment accompanied by the amendment topology, to enhance the robustness of the robot system through fast compliance control of the foot-end kinematics state. Then, the sliding mode control method based on consensus velocity and position error is deduced in LCC. The sliding mode surface is designed to make the three control variables realize stable movement with a consistent state of foot-end in three X,Y,Z-axis respectively, thereby enhancing the stability of foot-end state and fuselage posture. Finally, simulation and experiments have verified that the proposed LCC can assist legged-robot perform relatively steady legged motion with continuously variable direction on various rugged roads. The body attitude Root Mean Square Error (RMSE) is quickly reduced by 81.0% compared with independent PI control. The LCC algorithm code is publicly available at https://github.com/bjmyX/LCC_code.

Optimizing passengers’ experience: A goal-oriented reinforcement learning speed control approach for urban railway trains

Prolonged vibration can be uncomfortable for passengers utilizing urban rail transit systems. This study proposes an automatic speed control framework for urban railway trains to reduce vertical vibrations experienced by passengers. We suggest the concept of the “segmented comfort speed limit” to represent the vertical passing comfort of oncoming sections. This speed limit is calculated from 1/3 octave band acceleration and smoothed through lag-type speed control mode. The deep deterministic policy gradient algorithm with hindsight experience replay mechanism (HER-DDPG) is designed, to balance safety, comfort, and energy efficiency driving. Verify the speed control framework based on HER-DDPG through the rail data collected from Dalian Metro Line 12. Compared with the DDPG-based model, the vertical comfort is improved by 2.34%, and the longitudinal acceleration and total energy consumption are reduced by 45% and 8.1%. Compared with the real-world train control trajectory, HER-DDPG improves vertical comfort by 9.76% and reduces energy consumption by 12.4%. The results show that the proposed framework can effectively improve the ride experience of passengers.

Light-regulated soliton dynamics in liquid crystals

Electrically powered solitons are particle-like field configurations in out-of-equilibrium nematics that have garnered significant interest. However, their random generation and lack of controllable motion have limited their application. Here, we present a reconfigurable optoelectronic approach capable of regulating the entire lifecycle of solitons by utilizing multi-strategy digital light projection to construct delicate patterning of virtual electrode. We demonstrate that optically actuated domains with diverse geometry enable the generation of multiple solitons and further allow in-situ formation of individual soliton by matching the light pattern to its dimension. Exquisitely engineered light intensity of patterns facilitates modulation of soliton velocity and transformation of propagating direction. The utilization of a light-guided channel enables the on-demand control of soliton trajectories along customized paths. Furthermore, dynamic light patterns that vary in space and time allow for collective motion such as migration, mimicking phototaxis in biological systems. This reconfigurable manipulation strategy, grounded in the photoconductive effect, proves highly versatile and effective in directing soliton dynamics, heralding the potential for their programmable control and offering a significant advantage in multitasking scenarios.

‘Firm but fair’? Migrant children’s rights through dramaturgy and nation branding in Norway and the UK

Applying nation branding literature and the work of Erving Goffman on dramaturgy to the situation of asylum-seeking children in Norway and in the UK, this paper develops a comparative framework for understanding why child rights appear to be de-prioritised in the current climate of ‘migration control’. The paper identifies historically grounded differential approaches towards child rights and children in the two countries, which currently appear to merge into a common trajectory of migration control, framed in terms of national security and economic productivity. It explores similar tensions in both countries between the discourses of national migration management on the one hand and children's welfare and rights on the other. It finds that universal rights which should protect the welfare of all children are limited and fragmented by ideas of nationalism and foreignness. Despite a more robust legal framework for child rights, Norway is on a similar pathway as the UK; a worrying indictment of how nations fulfil their obligations towards children.

Research on ground mobile robot trajectory tracking control based on MPC and ANFIS

This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots.

Reinforcement Twinning: From digital twins to model-based reinforcement learning

The concept of digital twins promises to revolutionize engineering by offering new avenues for optimization, control, and predictive maintenance. We propose a novel framework for simultaneously training the digital twin of an engineering system and an associated control agent. The training of the twin combines methods from adjoint-based data assimilation and system identification, while the training of the control agent combines model-based optimal control and model-free reinforcement learning. The training of the control agent is achieved by letting it evolve independently along two paths: one driven by a model-based optimal control and another driven by reinforcement learning. The virtual environment offered by the digital twin is used as a playground for confrontation and indirect interaction. This interaction occurs as an “expert demonstrator”, where the best policy is selected for the interaction with the real environment and “cloned” to the other if the independent training stagnates. We refer to this framework as Reinforcement Twinning (RT). The framework is tested on three vastly different engineering systems and control tasks, namely (1) the control of a wind turbine subject to time-varying wind speed, (2) the trajectory control of flapping-wing micro air vehicles (FWMAVs) subject to wind gusts, and (3) the mitigation of thermal loads in the management of cryogenic storage tanks. The test cases are implemented using simplified models for which the ground truth on the closure law is available. The results show that the adjoint-based training of the digital twin is remarkably sample-efficient and completed within a few iterations. Concerning the control agent training, the results show that the model-based and the model-free control training benefit from the learning experience and the complementary learning approach of each other. The encouraging results open the path towards implementing the RT framework on real systems.

Sunflower-Inspired Superhydrophobic Surface with Composite Structured Microcone Array for Anisotropy Liquid/Ice Manipulation.

Precisely controlling the directional motion trajectories of droplets on anisotropic 3D functional surfaces has great application potential in self-cleaning, drug delivery, and droplet power generation, but it also faces huge challenges. Herein, inspired by the microcone structure in the heart of sunflowers, a nanoneedle-modified microcone array surface (NMAS)is reported. The surface is created using a combination of nanosecond laser direct engraving and electroforming and is subsequently fluorinated. Through programmable control of the laser spot, the geometric parameters and inclination angle of the microcone can be quickly and finely adjusted, thereby achieving precise control of the droplet bouncing trajectory. The results show that droplets can achieve programmable multiple bouncing behaviors on patterned functional surfaces, including gravity-defying hopping and directional water transport. It is worth noting that this functional surface has delayed freezing and anti-freezing effects. Furthermore, this functional surface has a wide range of potential applications, including surface self-cleaning, droplet capture, and droplet-based chemical microreactions, especially in the field of anti-icing operations. This opens up a new way for the directional transport of droplets on biomimetic functional surfaces.

State Feedback Via Judicious Pole Placement and Linear Quadratic Regulator – Application to Rotary Inverted Pendulum

In the control system regulatory concept, placing closed loop poles too far from the origin in the stability region produces fast regulation time but requires huge forcing energy as a trade-off. As such, stabilizing an unstable system with minimum energy is needed, though this presents a challenge to the designer. At the design phase, the designer may ponder the optimized energy while compromising the possibility of catastrophic stabilization phenomena due to minimal forcing thrust towards the poles. In this manuscript, a simple Linear Quadratic Regulator (LQR) is proposed as an alternative to full state feedback (FSF) with judicious pole placement. The efficacy of both approaches was observed by exploiting a Rotary Inverted Pendulum (RIP) as a testbed. Beforehand, the RIP system dynamics were developed in the time domain. RIP is an under-actuated mechanical system that is inherently nonlinear and unstable. The main control objectives of RIP are swing-up control, stabilization control, switching control, and trajectory control. The methodology involved the appearance of weighted matrices that were necessary for the minimum cost function. The Riccati and Lyapunov criteria are also exploited to facilitate design. The result shows the comparative transient performances of the two approaches, where the LQR outperforms the FSF in many aspects.

An Advanced Control Method for Aircraft Carrier Landing of UAV Based on CAPF–NMPC

This paper investigates a carrier landing controller for unmanned aerial vehicles (UAVs), and a nonlinear model predictive control (NMPC) approach is proposed considering a precise motion control required under dynamic landing platform and environment disturbances. The NMPC controller adopts constraint aware particle filtering (CAPF) to predict deck positions for disturbance compensation and to solve the nonlinear optimization problem, based on a model establishment of carrier motion and wind field. CAPF leverages Monte Carlo sampling to optimally estimate control variables for improved optimization, while utilizing constraint barrier functions to keep particles within a feasible domain. The controller considers constraints such as fuel optimization, control saturation, and flight safety to achieve trajectory control. The advanced control method enhances the solution, estimating optimal control sequences of UAV and forecasting deck positions within a moving visual field, with effective trajectory tracing and higher control accuracy than traditional methods, while significantly reducing single-step computation time. The simulation is carried out using UAV “Silver Fox”, considering several scenarios of different wind scales compared with traditional CAPF–NMPC and the nlmpc method. The results show that the proposed NMPC approach can effectively reduce control chattering, with a landing error in rough marine environments of around 0.08 m, and demonstrate improvements in trajectory tracking capability, constraint performance and computational efficiency.

Trajectory control and optimization of PID controller parameters for dual-arms with a single-link underwater robot manipulator

ABSTRACT This paper focuses on the modeling, simulation, and control of the trajectories of a dual-arm single-link underwater robot manipulator (DS-URM), having dual arms (left and right) with a single link each. The dynamic coupling between the base and the dual arms makes trajectory control of the end effector difficult. This study attempts to simulate and control dual arms with a single-link underwater robotic manipulator using a hybrid controller. To reduce the error for a sinusoidal wave and a cycloid trajectory, a hybrid controller is the combination of the Proportional-Integral-Derivative (PID) controller and overwhelm controller. The underwater dynamic’s buoyancy, gravity, and hydrostatic forces acting on the underwater robot are modeled in an integrated SIMULINK model, including the DS-URM. The DS-URM has been investigated, revealing some significant trajectories of the left and right tips. Effective tuning methods include Ziegler-Nicholas (Z-N), genetic algorithms, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). The Integral of Time multiplied by Absolute Error (ITAE) criteria has been used to improve trajectory tracking via particle swarm optimization. Results show significant improvements in trajectory tracking, with ITAE error reduction by 96.44% and 98.6% for left and right arms, respectively, achieving stability in 0.000645s.

Mapping structured Laguerre–Gaussian beam states onto the orbital Poincaré sphere in the form of controllable spatial trajectories

The structured Laguerre–Gaussian (LG) beam is a two-parameter superposition of 2n+ℓ+1 Hermite–Gaussian modes (where n and ℓ are a radial number and a topological charge of the initial LG beam) whose orbital angular momentum oscillations are controlled by phases and amplitude parameters. But we succeeded in reducing its representation to a simple sum of a standard LG mode and a hybrid Hermite–Laguerre–Gaussian (HLG) beam that is a key point in understanding a hidden geometry of the structured LG (sLG) beams and implementations of its unique prosperities. In assents, the hybrid HLG beam is mapped onto the orbital Poincaré sphere in the form of a plane trajectory along a main meridian of the sphere. However, the most intriguing thing is as follows. First, once we slightly perturb the HLG beam with a single LG mode, the flat trajectory turns into a complex multi-petalled tracery with multiple self-intersections due to cyclic variation of the phase parameter of the sLG beam. Moreover, the shape of the tracery as well as the birth and destruction of the self-intersection points can be controlled with the amplitude parameter. However, it is worth noting that when changing the beam parameters cyclically, the area outlined by the trajectory on the sphere is directly related to the geometric phase acquired by the sLG beam that can be treated as an additional degree of freedom for transmitting big data. In the article, we study the sLG beam properties and its mapping onto the orbital Poincarè sphere in the framework of a symplectic 4×4 matrix formalism while the orbital Stokes parameters are experimentally measured, and we have found good agreement between theory and experiment.

Achieving Energy-Efficient and Travel Time-Optimized Trajectory and Signal Control for CAEVs

Achieving Energy-Efficient and Travel Time-Optimized Trajectory and Signal Control for CAEVs

Model Predictive Trajectory Optimization and Control for Autonomous Surface Vessels Considering Traffic Rules

Model Predictive Trajectory Optimization and Control for Autonomous Surface Vessels Considering Traffic Rules

Flow field design and experiment of high-efficiency electrochemical milling with large machining area

Materials that are difficult to cut, such as titanium alloys, are widely used in large load-bearing integral components of aircraft, leading to great challenges for manufacturing. Electrochemical milling is a way for machining difficult-to-cut materials through Computer Numerical Control (CNC) trajectory motion. Using a tilted large cathode machining surface and the cut-in feed mode, an efficient and low-cost method is obtained for machining the large integral components. A novel crossed and inclined structure of the flow mode is designed to realize electrochemical milling with a large tilted cathode surface. Compared to the vertical flow mode with one inlet, the proposed flow mode has two inlets that independently supply electrolytes, and the inclined channels make the flow field more stable. Flow field simulations are performed for both the vertical and proposed flow modes. The results show that the proposed flow mode avoids the random diversion of electrolytes and the ultralow flow velocity at both ends of the nozzle area, improving the velocity, uniformity, and stability of the electrolytes. The inclination angle of the crossed and inclined flow field is optimized. Finally, limit feed rate experiments are conducted in two modes, and the limit feed rate is 70 mm/min in the proposed mode. A sector workpiece of a large circular surface with approximately 8.77 mm thickness is machined 9 times by the cut-in electrochemical milling, the material removal rate is 4872 mm3/min, and the surface roughness is superior to 1.15 μm.

Application and Performance Evaluation of Backstepping Control for Drone 3D Trajectory Control

Application and Performance Evaluation of Backstepping Control for Drone 3D Trajectory Control

Trajectory control strategy for multi-tool synchronous electrochemical machining of blisk channels

The blisk is a core component of an aero-engine, and electrochemical machining (ECM) is the primary method for its manufacture. Among several ECM methods for blisks, multi-tool synchronous machining is the most efficient and advantageous for machining channels. The allowance distribution of the blank after blisk channel machining directly influences the blade profile accuracy. This paper proposes a trajectory control strategy to homogenize the allowance distribution of the blisk channel in multi-tool ECM. The strategy includes the design of the three-dimensional space motion of the tool and blisk, as well as the regulated feed speed. The structural characteristics of the blisk channel and the principle of ECM allow for designing and optimizing the multi-dimensional trajectory. The electric field simulations elucidate the influence law of the three-axis feed speed on the side gap. An algorithm is adopted to iteratively optimize the speeds for different positions to realize multi-dimensional motion control and allowance homogenization. The proposed trajectory control strategy is applied to ECM experiments for the blisk channel. Compared with the constant feed speed mode, the regulated speed strategy reduces the maximum allowance difference between the convex (CV) profiles by 36.18% and that between the concave (CC) profiles by 37.73%. Subsequently, the one-time ECM of eight blisk channels was successfully realized. The average time for a single channel was 12.5 min, significantly improving the machining efficiency. In conclusion, the proposed method is effective and can be extended for synchronously machining various blisk types with twisted channels.

Controlling a Quadcopter with Static Loads and Dynamic Wind Disturbances using a Fuzzy Inference System

Maneuverability, hover, and simple mechanical design are the advantages of quadcopters. However, because quadcopters are smaller and lighter, they are more susceptible to wind than manned aircraft. The winds that cause air accidents are divided into several categories, namely downburst, turbulent wind, wind shear, and wind vortices. Disturbances and uncertainties, such as wind gusts, can result in difficulties in executing a mission on an accurate flight path. Quadcopter resilience is an important topic for UAV. Especially if the quadcopter is in terrain that is difficult for humans to reach. Hence, the system is susceptible and experiences reduced stability. Controlling a quadcopter with a cube-shaped static load to withstand turbulent wind gusts in this research uses robust Fuzzy Inference System control and trajectory control using LQR with Command-Generator Tracking. The results achieved through fuzzy control can fortify the quadcopter against half of the overall turbulent wind gusts with an RMSE of 0.0546. In contrast with the LQR-CGT control, which still exhibits an RMSE of 0.0795.

Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed.

An overactuated aerial robot based on cooperative quadrotors attached through passive universal joints: Modeling, control and 6-DoF trajectory tracking

This article discusses a novel aerial robot architecture that overcomes the underactuation of conventional multirotor systems without adding dedicated rotor tilting actuators. The proposed system is based on four quadrotors cooperatively carrying a central body to which they are attached through passive universal joints. While conventional parallel axis multirotors are underactuated, the proposed mechanism makes the system overactuated, enabling independent position and orientation control of the main body. This implies that the payload can be carried in the minimum drag orientation, it enables take-off and landing on inclined surfaces and it provides thrust-vectoring capabilities to the system, leading to high control authority. A detailed dynamic model is derived making use of Lagrangian formalism and a hierarchical control law based on such model is proposed to stabilize the system. This control law is designed to ensure good tracking while minimizing power consumption. The proposed control law and the capabilities of the architecture are evaluated in simulation and in outdoor experimental flights, where the aerial robot shows autonomous tracking of the six degrees of freedom (DoF) of the main body, an inherently unfeasible maneuver for conventional underactuated multirotors.

Parameter ESTimation With the Gauss-Levenberg-Marquardt Algorithm: AnIntuitive Guide.

In this paper, we review the derivation of the Gauss-Levenberg-Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.